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    Platform For AI:LVM-Image-Aesthetic Filter (DLC)

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    Platform For AI:LVM-Image-Aesthetic Filter (DLC)

    Last Updated:Jul 23, 2026

    The LVM-Image-Aesthetic Filter (DLC) component is used to filter the data of an image that has a low aesthetic score.

    Supported computing resources

    Deep Learning Containers (DLC)

    Algorithm

    The LVM-Image-Aesthetic Filter (DLC) component calculates aesthetic scores of images and filters the data of an image that has an excessively low aesthetic score to ensure the quality of the image. In most cases, the component is used for the subsequent training of image generation models.

    Inputs and outputs

    Input ports

    • The Read File Data component is used to read the Object Storage Service (OSS) path in which the training data is stored.

    • You can configure the Image Data OSS Path parameter to select the OSS directory where the image data is stored or select the image metadata file. For more information, see the parameter description in the following section.

    • You can use any component for image data preprocessing as the input.

    Output port

    The filtering results. For more information, see the parameter description in the following section.

    Configure the component

    You can configure the parameters of the LVM-Image-Aesthetic Filter (DLC) component in Machine Learning Designer. The following table describes the parameters.

    Tab

    Parameter

    Required

    Description

    Default value

    Field Settings

    Image Data OSS Path

    No

    If no upstream component exists the first time you run this component, you must manually select the OSS directory in which the image data is stored. When the component runs, the image metadata file meta.jsonl is generated in the upper-level directory of the directory specified by this parameter. When you use the component to process the image data later, you can directly select the file meta.jsonl.

    No default value

    OSS Directory for Saving OutputData

    No

    The OSS directory in which the filtering results are stored. The results include the following files:

    • {name}.jsonl: the output file. You can configure the Output Filename parameter to specify the output file.

    • {name}_stats.jsonl: the state file.

    • dj_run_yaml.yaml: the parameter configuration file used when the algorithm runs.

    No default value

    Output Filename

    No

    The file name of the filtering results.

    result.jsonl

    Parameter Settings

    Minimum Aesthetic Score

    Yes

    The minimum aesthetic score.

    0.3

    Maximum Aesthetic Score

    Yes

    The maximum aesthetic score.

    1

    Execution Tuning

    Number of Processes

    No

    The number of processes.

    4

    single/multi nodes

    No

    Single-node or multi-node mode. Default value: single-node. Multi-node mode supports only the Ray framework.

    single-node

    Select Resource Group

    No

    Select a public resource group or a resource quota (if distributed mode is used, you must set resource parameters for the head and worker nodes separately):

    • Public resource group: select an instance type (CPU or GPU) and a VPC. You must select a GPU instance type for the algorithm.

    • Resource quota: select the number of GPUs, number of vCPUs, memory (GB), and shared memory (GB).

    No default value

    Maximum Running Duration (seconds)

    No

    The maximum period of time for which the component can run. If the specified period of time is exceeded, the job is terminated.

    No default value